The Black–Scholes Option pricing model (OPM) developed in 1973 has always been taken as the cornerstone of option pricing model. The generic applications of such a model are always restricted by its nature of not being suitable for fuzzy environment since the decision-making problems occurring in the area of option pricing are always with a feature of uncertainty. When an investor faces an opti...

Contrasting to Description Logics (DLs), there are some inherent shortcomings in classical fuzzy Description Logics (FDLs). For example, they no longer satisfy the complementary laws. In this paper, to analyze these shortcomings derived from Zadeh semantics, an improved fuzzy set definition using Boolean lattices is approached, whereas, the analogous definition of Zadeh Fuzzy Set can only use a...

Fuzzy set theory opens new vistas in computability theory and here I show this by defining a new computational metaphor—the fuzzy chemical metaphor. This metaphor is an extension of the chemical metaphor. In particular, I introduce the idea of a state of a system as a solution of fuzzy molecules, that is molecules that are not just different but rather similar, that react according to a set of ...

Hyers-Ulam-Rassias stability have been studied in the contexts of several areas of mathematics. The concept of fuzziness and its extensions have been introduced to almost all branches of mathematics in recent times.Here we define the cubic functional equation in 2-variables and establish that Hyers-Ulam-Rassias stability holds for such equations in intuitionistic fuzzy Banach spaces.

A risk-minimizing portfolio model under uncertainty with randomness and fuzziness is discussed. By a perception-based extension of estimations for fuzzy random variables, the risk-minimizing portfolio problem is developed. In the uncertainty model, the randomness and fuzziness are evaluated respectively by the probabilistic expectation and mean values with evaluation weights and -mean functions...

Fuzzy Logic is extended toward dynamic adaptation of the degree of fuzziness. The motivation is to explain the process of learning as joint model improvement and fuzziness reduction. A learning system with fuzzy models is introduced. Initially, the system is in a highly fuzzy state of uncertain knowledge, and it dynamically evolves into a low-fuzzy state of certain knowledge. We present an imag...

In this paper we make several contributions to the literature of poverty measurement. We …rst identify a most plausible source for the fuzziness in poverty identi…cation which renders a natural and direct estimation of the poverty membership function; we then provide an axiomatic characterization for an important class of fuzzy poverty measures; and we …nally derive a set of crisp dominance con...

Fuzzy clustering techniques handle the fuzzy relationships among the data points and with the cluster centers (may be termed as cluster fuzziness). On the other hand, distance measures are important to compute the load of such fuzziness. These are the two important parameters governing the quality of the clusters and the run time. Visualization of multidimensional data clusters into lower dimen...